Association between inflammation-based prognostic markers and mortality after hip replacement

We aimed to evaluate the association between inflammation-based prognostic markers and mortality after hip replacement. From March 2010 to June 2020, we identified 5,369 consecutive adult patients undergoing hip replacement with C-reactive protein (CRP), albumin, and complete blood count measured within six months before surgery. Receiver operating characteristic (ROC) curves were generated to evaluate predictabilities and estimate thresholds of CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Patients were divided according to threshold, and mortality risk was compared. The primary outcome was one-year mortality, and overall mortality was also analyzed. One-year mortality was 2.9%. Receiver operating characteristics analysis revealed areas under the curve of 0.838, 0.832, 0.701, and 0.732 for CAR, NLR, PLR, and modified Glasgow Prognostic Score, respectively. The estimated thresholds were 2.10, 3.16, and 11.77 for CAR, NLR, and PLR, respectively. According to the estimated threshold, high CAR and NLR were associated with higher one-year mortality after adjustment (1.0% vs. 11.7%; HR = 2.16; 95% CI 1.32–3.52; p = 0.002 for CAR and 0.8% vs. 9.6%; HR = 2.05; 95% CI 1.24–3.39; p = 0.01 for NLR), but PLR did not show a significant mortality increase (1.4% vs. 7.4%; HR = 1.12; 95% CI 0.77–1.63; p = 0.57). Our study demonstrated associations of preoperative levels of CAR and NLR with postoperative mortality in patients undergoing hip replacement. Our findings may be helpful in predicting mortality in patients undergoing hip replacement.


Study population and data collection
We enrolled adult patients who underwent hip replacement surgery at Samsung Medical Center between March 2010 and June 2020.Patients without available CRP, albumin level, and complete blood cell count within six months before surgery were excluded.Data were extracted in a de-identified form using the electronic archive system, "Clinical Data Warehouse Darwin-C, " which allows retrieval of data from electronic hospital records containing over 2.2 million surgeries, one billion lab results, 100 million disease codes, and 200 million prescriptions.Blood test results were automatically analyzed.Mortality data were regularly checked and updated using the National Population Registry of the Korea National Statistical Office to ensure no missing information on deaths.Medical records were reviewed by investigators who were unaware of patient mortality to prevent bias.

Study outcomes and definitions
The primary study endpoint was mortality during one-year follow-up after surgery, and mortality during threeyear follow-up was also compared.
Three prognostic indicators were determined using the following formulas: NLR = absolute neutrophil count / absolute lymphocyte count, PLR = absolute platelet count / absolute lymphocyte count, CAR = CRP / albumin.Additionally, mGPS was estimated from baseline CRP and albumin levels using the following calculation: Score 0: CRP ≤ 10 mg/L, Score 1: CRP > 10 mg/L and albumin ≥ 3.5 g/dL, and Score 2: CRP > 10 mg/L and albumin < 3.5 g/ dL 18 .The Charlson comorbidity index was calculated using the 10 th revision of the International Statistical Classification of Diseases and Related Health Problems-10 19 .

Statistical analysis
In this study, we used a number of statistical methods to analyze the data collected from their cohort of patients who underwent hip replacement.The categorical variables, such as the patients' sex, were presented in terms of numbers and percentages, while continuous variables, such as age and preoperative blood laboratory tests, were expressed as either means and standard deviations or medians and interquartile ranges, depending on the most appropriate measure of central tendency.
To compare the categorical variables, the researchers used the Chi-square test, and for continuous variables, we used the t-test or the Mann-Whitney test.To determine the optimal cut-off values of the biomarkers (CAR, NLR, and PLR) associated with one-year mortality, we performed receiver operating characteristics (ROC) curve analysis and calculated Youden's index.Then we compared the ROC curves using DeLong's test 20 .After dividing the patients into low and high groups based on the estimated cut-off values, the researchers used Cox regression analysis to compare their mortalities, reporting the results as hazard ratios with 95% confidence intervals (CI).To reduce bias and achieve balance between groups, we conducted an adjustment using inverse probability weighting (IPW) with propensity scores for all relevant variables 21 .All statistical analysis was performed using R version 4.2.0, and a p-value less than 0.05 was considered statistically significant.

Ethics approval and consent to participate
This retrospective cohort study was approved by the Institutional Review Board at Samsung Medical Center (SMC 2023-01-060).The requirement for written informed consent was waived because the data were collected retrospectively in de-identified form.

Results
A total of 5,369 patients with available blood laboratory test results were included in the analysis.The overall study flowchart is shown in Fig. 1.Among them, 154 patients (2.9%) experienced one-year mortality.The Table 1 revealed notable differences between patients who experienced one-year mortality and those who did not.The former group tended to be older, predominantly male, and exhibited a higher prevalence of comorbidities.Additionally, emergency surgeries under general anesthesia were more frequent among patients with one-year mortality.Preoperative median values of CAR, NLR, PLR, and the proportion of patients with mGPS = 2 were significantly higher in the one-year mortality group.
Optimal cut-off threshold values for one-year mortality were determined based on the maximum Youden's index: 2.10 for CAR, 3.16 for NLR, and 11.77 for PLR.Using these cut-off values, positive and negative predictive values were calculated for each marker.Subsequently, patients were classified into low and high groups for CAR (4,413 vs. 956), NLR (4,100 vs. 1,269), and PLR (4,085 vs. 1,284).Baseline characteristics were compared between these groups in Tables 2, 3, 4, revealing consistently higher values and greater incidence of relevant risk factors in the high groups.

Discussion
In our study, we investigated the relationship between various preoperative biomarkers and the risk of mortality after hip replacement surgery.The biomarkers that were evaluated were the CAR, NLR, and PLR.Our results showed that preoperative CAR and NLR were both strongly associated with higher mortality after hip replacement surgery.The predictive power of these two markers was found to be comparable, meaning that either one could effectively predict the risk of patient mortality.However, when patients were divided into groups based on their preoperative PLR values, no significant association was found between PLR and patient mortality.Overall, our study suggests that preoperative CAR and NLR may be valuable tools for predicting patient mortality after hip replacement surgery.The findings of this study may be useful for clinicians in making informed decisions about patient care and prognosis.Inflammation is a crucial aspect of various diseases and has been shown to be a powerful predictor of patient outcomes in a wide range of medical conditions [6][7][8][9][10][11][12][13] .In the field of orthopedics, systemic inflammation has been linked to poor outcomes such as periprosthetic joint infections and surgical site infections 22,23 .To better understand the state of the systemic inflammatory response, various markers have been proposed and studied.White blood cells play a critical role in this response and previous studies have shown that the number of neutrophils in circulation increases while the number of monocytes and lymphocytes decreases during inflammation 24 .Additionally, low levels of lymphocytes are thought to indicate physiological stress and poor outcomes 25 .CRP is also a marker of acute-phase inflammatory response, and low level of albumin indicates malnutrition as well as inflammation severity and disease progression 26 .Infection, comorbidities, and malnutrition have been found to www.nature.com/scientificreports/cause inflammation and decline in nutritional status, which is likely to be found in frail patients [26][27][28] .Together, a combination of these values can accurately reflect the mortality rate after hip replacement surgery, which is primarily performed on frail elderly patients.Our results showed that the estimated cut-off points had high negative predictive value, suggesting that these markers could be helpful in identifying those at relatively low risk and preserving the limited resources for other patients.
A key strength of this study lies in its accessibility and clinical relevance.By leveraging readily available blood markers from routine laboratory tests, we offer a practical approach for risk prediction without requiring additional specialized assays.Moreover, constructing composite markers from existing tests offers advantages in capturing the complex interplay between inflammation and mortality.Comparing the predictive performance of individual and combined markers, as assessed by AUC, highlights the potential of combining readily available data for improved risk stratification.
To further explore more intricate relationships between genetic and inflammatory markers, interaction prediction research in various fields of computational biology has provided promising avenues for future investigation.Techniques like deep learning-based models capable of identifying complex epistatic and non-additive interactions between genes and environment could offer valuable insights into personalized risk assessment for conditions with complex inflammatory involvement, including post-surgical outcomes.Notably, recent studies employing such approaches demonstrate the potential of these methods for unraveling intricate genetic interactions [29][30][31][32][33][34][35] .In addition, integrating our findings with future ordinary differential equation (ODE)-based models that incorporate specific inflammatory markers identified in this study could offer a deeper understanding of the dynamic interplay between inflammation and post-surgical outcomes 36,37 .By combining the strengths of readily available clinical data with the mechanistic insights provided by theoretical modeling, we can pave the way for more comprehensive risk prediction models and ultimately, the development of personalized preventative and therapeutic strategies.
According to PLR showed a lower predictive power compared with CAR and NLR, and patients with high PLR did not show increased risk of mortality after statistical adjustment.In contrast with our results, previous studies of patients undergoing hip surgery found a correlation between preoperative PLR and postoperative mortality 17,38 .However, despite these similarities, the study patients had some important differences from our patient population.Most previous studies evaluating the correlation between preoperative PLR and hip surgery outcomes have been conducted in elderly populations.However, our study included patients of all ages, with a relatively younger patient population compared to these previous studies.This difference in age range is significant as the influence of age on the outcome of hip replacement surgery is well documented.Additionally, the previous studies that evaluated the relationship between preoperative PLR and hip surgery outcomes also enrolled patients of all ages, but their patient populations were older compared to our study population.These differences in patient characteristics suggest that the results of our study may have a different implication for clinical practice compared to the results of these previous studies 17,38 .
Another factor to consider is that platelets may not be as sensitive a marker as the other biomarkers studied, and therefore may not be able to accurately reflect the health status of patients who are relatively healthier.This is supported by the results of a subgroup analysis from a previous study, which found that there was no significant correlation between preoperative PLR and hip surgery outcome in patients without anemia.This suggests that platelets may not be an ideal marker for these types of patients, and other markers may be more effective in predicting outcomes in this population 17 .It is also possible that there exists a publication bias, where only studies with positive results are published and reported.This phenomenon, known as the "file drawer problem," could www.nature.com/scientificreports/potentially skew the interpretation of the results regarding the predictive value of PLR.Our study results imply that PLR may not be a universally effective predictor of patient outcomes after hip replacement surgery and that it should be used with caution.Further research is necessary to determine the specific patient populations for which PLR may have the greatest utility as a prognostic marker.There are several limitations to consider when interpreting our study results.First, because this is a retrospective single-center study, our results may be influenced by unidentified factors, despite statistical adjustments.Second, the long study period may have introduced changes in surgical techniques and postoperative care that www.nature.com/scientificreports/could have influenced results.Third, our study does not provide any treatment recommendations for patients with elevated biomarkers, and it is not possible to establish an optimal threshold from our analysis.Additionally, the positive predictive value of the markers was relatively low, indicating that they may not be as effective at identifying patients who are at high risk and should be used in conjunction with other clinical information.A prospective randomized trial is necessary to further validate these findings.Despite these limitations, this is the first study that comprehensively examines biomarkers in relation to mortality outcomes after hip replacement surgery.In conclusion, our study demonstrated associations of preoperative levels of CAR and NLR with postoperative mortality in patients undergoing hip replacement.Our findings suggest that further studies are needed for these markers to be useful in identifying patients with high mortality risk and for developing strategies to improve outcomes after hip replacement surgery.

Table 1 .
Baseline characteristics according to one-year mortality.Values are n (%), mean (± standardized deviation), or median (interquartile range).ASD absolute standardized mean difference, IPW inverse probability weighting.*Following variables were retained for IPW adjustment.

Table 2 .
Baseline characteristics according to the estimated cut-off point of C-reactive protein/albumin ratio > 2.10.Values are n (%), mean (± standardized deviation), or median (interquartile range).ASD absolute standardized mean difference, IPW inverse probability weighting.*Following variables were retained for IPW adjustment.

Table 4 .
Baseline characteristics according to the estimated cut-off point of platelet/lymphocyte ratio > 11.77.Values are n (%), mean (± standardized deviation), or median (interquartile range).ASD absolute standardized mean difference, IPW inverse probability weighting.*Following variables were retained for IPW adjustment.